Research and development at the intersection of user experience, security and privacy, and decentralized infrastructure. Goal: publish reproducible UX research, prototypes, and tools that shorten time-to-value in healthcare, cybersecurity, DePIN, and accessible AI.

• Patient intake, surgical coordination, clinician decision support under cognitive load
• Accessibility in clinical UIs (WCAG 2.2 AA), audit trails, safety-critical interaction
• Data handling without PHI leakage; de-identification and synthetic data workflows

• Usable cryptography: key management, MFA, threshold shares and recovery
• Interfaces for consent, auditability, data minimization, and policy enforcement
• Operator tooling to reduce misconfiguration; explainability of security outcomes
• Threat and privacy modeling applied to UX (STRIDE, LINDDUN)

• Operator dashboards for GPU, storage, bandwidth participation; resource scheduling
• Observability of decentralized systems: reliability, rewards, faults, latency
• Incentive design surfaced in UI; failure-mode communication and recovery paths

• AI-assisted accessibility audits and usability clustering for large datasets
• Privacy-preserving ML: federated learning, differential privacy, zkML
• Prototypes for rapid deployment with minimal adoption barriers
• Evaluation of user trust, explainability, and transparency in AI-driven systems

• Studies: protocols, instruments, sanitized or synthetic datasets, analysis notebooks
• Prototypes: minimal working systems that expose interaction patterns before scale
• Tooling: auditors, linters, collectors, visualization components, test harnesses
• Curations: “awesome” lists, standards mappings, reference implementations

• Empirical design: task analyses, cognitive walkthroughs, SUS, NASA-TLX, time-on-task, error rate; inter-rater agreement (Cohen’s kappa)
• Pre-registered protocols where relevant; A/B and counterfactual evaluation
• Reproducibility: deterministic seeds, pinned environments (requirements.txt, package-lock.json), container specs (Dockerfile), data schemas
• Security and privacy alignment: NIST SP 800-63/53, HIPAA 45 CFR Part 164, GDPR Art. 5/25, ISO 9241-210, IEC 62366, WCAG 2.2
• Adoption metric focus: install → first insight (TTFI) in minutes

• ux-methods — study templates, consent language, instruments, analysis notebooks
• ai-ux-auditor — AI-assisted accessibility and usability audit CLI with report artifacts
• healthcare-ux — patient and clinician workflow prototypes with safety checks and logs
• security-ux — key, MFA, and consent flows; error-proofing, recovery, threat models
• depin-ux — operator dashboards, schedulers, reliability and reward visualizations
• ai-research-demos — federated learning, differential privacy, zkML examples with UX harnesses
• awesome-accessible-ai — curated research, standards, and tooling

• Code: MIT by default. Docs and data: CC BY 4.0. Models: Apache-2.0 unless noted
• Each repo declares dataset license, schema, provenance, and limitations
• DOIs via Zenodo for citable releases; semantic versioning for artifacts

• Evidence over assertion; publish limitations and negative results
• No sensitive data; PHI and PII prohibited. Use de-identification or synthetic generation
• Issues and PRs reference tasks, metrics, and standards mappings